Apparatus, method, and computer program product for converting decision flowcharts into decision probabilistic graphs

ABSTRACT

The present invention converts decision flowcharts into decision probabilistic graphs on a data processing system. First, a decision flowchart is received, having evidence nodes, a root evidence node, and outcome nodes. The outcome nodes are related to the evidence nodes by conclusion links. Next, an operation is performed, generating a probabilistic graph based on the flowchart. The graph includes an aggregate outcome node having outcome states, with each outcome state representing an outcome node of the flowchart; a plurality of test nodes, each matching an evidence node in the flowchart, and each test state matching a conclusion link from the evidence node in the flowchart, and causal links between the aggregate outcome node and the evidence nodes. Prior probabilities are calculated for outcome states based on predetermined likelihoods. Conditional probabilities are determined for test states by examining dependencies of conclusion links on the outcome nodes in the decision flowchart.

BACKGROUND

(1) Technical Field

The present invention relates to the construction of decisionprobabilistic models, and their derivation, and more particularly to atool for converting decision flowcharts into decision probabilisticmodels.

(2) Discussion

Most of the decision systems used in practice are based on decisionflowcharts or databases of decision cases. These techniques have manyshortcomings when compared with probabilistic graphical models, whichare generally advocated for advanced decisions. They are much lessaccurate, less flexible, harder to maintain, and their complexity growsexponentially with the size of a diagnosed system. However, conventionaldecision flowcharts exist for many systems, and experts typicallycapture decision knowledge in the form of flowcharts.

Conventional decision flowcharts are very popular tools with severedecision limitations and high creation and modification costs. Despitethe fact that decision flowcharts are the most common graphical form forexpressing decision procedures, their design is time-consuming andrequires extensive expertise in diagnosing a system for which it isintended. Such flowcharts consist of ordered observations (e.g.,symptoms, error messages, and tests) and failures. The user follows afixed sequence of observations and arrives at the root-cause failure.The procedure does not allow the user to divert from the prescribedsequence of observations. Creating the flow charts is a time-consumingprocess requiring advance decision expertise. Once created, theflowcharts are difficult to modify and maintain, with the result thateliminating or adding observations or failures to the flowchart oftenrequires a complete redesign. Therefore, in practice, theflowchart-based decision tools rapidly become out-of-date.

Graphical probabilistic models are decision models of decisions andobservations. Expert knowledge or decision data is used to create thegraphical probabilistic model. The model captures causal dependenciesbetween the decisions and the observations. They can produce dynamicdecision procedures, when used with algorithmic engines. At each step ofthe decision sequence, the user has full flexibility in choosing thenext observation to perform.

Decision flowcharts for diagnostic applications diagnose onlysingle-defect failures. Their design assumes that one and only onecomponent failed; and therefore, a single deterministic sequence oftests will lead to the proper conclusion, indicating which component isthe root-cause of the failure. When the flowchart is used in diagnosis,the user has to adhere strictly to the sequence of observationsprescribed in it. The flowchart has to be abandoned as a decision toolif, at some point at the observation sequence, the user is not able toperform the specified observation.

Graphical probabilistic models are a much more powerful form ofexpressing decision knowledge. They are much more flexible and usefulthan flowcharts and are much easier to modify. One can explicitlyexpress effectiveness and cost of observation information in them.During diagnosis, an algorithmic engine uses a probabilistic model toproduce ranked failures and ranked observations each time a newobservation is made. The engine dynamically generates a sequence ofobservations, which are optimized for convergence to root-cause and forcost. The user has full flexibility in choosing which of the rankedobservations to perform at each stage of the diagnosis.

Currently, there exists a need for a tool that automatically converts anexisting flowchart into a graphical probabilistic model. Such a tool isparticularly desirable in order to create powerful graphicalprobabilistic models in order to produce better decision procedures. Afurther advantage of such a conversion tool is that it would enable auser to take advantage of the flexibility of use and ease ofmodification that is possible with graphical models and impossible withconventional decision flowcharts.

SUMMARY

The present invention provides a method, a computer program product, andan apparatus for converting decision flowcharts into decisionprobabilistic graphs on a data processing system. The method comprises aset of steps to be performed on a data processing system, the computerprogram product comprises a set of computer operations encoded on acomputer-readable medium, and the apparatus comprises a computer systemincluding a processor, a memory coupled with the processor, an inputcoupled with the processor for receiving user input and data input, andan output coupled with the processor for outputting display data.

The operations performed by the invention, in a first aspect, includereceiving a representation of a decision flowchart having evidencenodes, a root evidence node, and outcome nodes. The outcome nodes arerelated to the evidence nodes by conclusion links. A further operationof generating a probabilistic graph based on the decision flowchart isperformed. The probabilistic graph generated includes an aggregateoutcome node having a plurality of outcome states, with each outcomestate representing an outcome node of the decision flowchart, and aplurality of test nodes with each of the test nodes matching an evidencenode in the decision flowchart, and each test state matching aconclusion link from the evidence node in the flowchart. Causal linksare created between the aggregate outcome node and the evidence nodes.Two additional operations in this aspect include calculating a set ofprior probabilities for the outcome states based on predeterminedlikelihoods; and determining conditional probabilities for all teststates by examining dependencies of conclusion links on the outcomenodes in the decision flowchart.

In a further aspect of the invention, a representation of the decisionflowchart is obtained as a Flowchart Markup Language (FCML) documentcontaining the essence of the decision flowchart.

In a still further aspect, the graphical representation of the decisionflowchart is a Bayesian Network (BN).

In another aspect, in the operation of calculating a set of priorprobabilities, an operation of generating a causal dependency tablecomprising a causal dependency of each test node on each outcome stateis performed.

In yet another aspect, the causal dependency table includes a separatecolumn for each outcome node of the decision flowchart and a separaterow for each evidence node of the decision flowchart; whereby aggregateentries of each column of the table traces a path from the root evidencenode to a particular outcome node.

In still another aspect, the operation of determining conditionalprobabilities further comprises an operation of generating, for eachevidence node, a conditional probability table comprising theconditional probability of each test state given each outcome state.

In another aspect, the invention includes an operation of outputting amodel file for the probabilistic graph to an algorithmic engine forfurther processing.

In a further aspect of the present invention, predetermined likelihoodsare inputted based on observed statistics.

In a still further aspect, the present invention comprises a furtheroperation of determining a next piece of evidence to gather based oncost-of-evidence data.

In yet another aspect, the present invention comprises an operation ofgenerating a representation of the decision flowchart via graphingsoftware for receipt in the receiving step.

In another aspect, the present invention comprises an operation ofgenerating a user interface to accept user input to the algorithmicengine whereby the user can control the order in which evidence iscollected.

In a further aspect, the present invention comprises an operation ofconverting the graphical representation of the decision flowchart into aBayesian network modeling program-specific file.

BRIEF DESCRIPTION OF THE DRAWINGS

The objects, features and advantages of the present invention will beapparent from the following detailed descriptions of the preferredaspect of the invention in conjunction with reference to the followingdrawings where:

FIG. 1 is a block diagram illustrating the context within which thepresent invention may be used;

FIG. 2 is a block diagram of a computer system for use with the presentinvention;

FIG. 3 is an illustrative diagram of a computer program product aspectof the present invention;

FIG. 4 is an illustration of an example decision flowchart;

FIG. 5 is an illustration of an example probabilistic graph derived fromthe decision flowchart shown in FIG. 4;

FIG. 6 is a causal dependency table derived from the decision flowchartshown in FIG. 4; and

FIG. 7 is a conditional probability table derived from the decisionflowchart shown in FIG. 4.

DETAILED DESCRIPTION

The present invention relates to the construction of decisionprobabilistic models, and their derivation, and more particularly to atool for converting decision flowcharts into decision probabilisticmodels. The following description is presented to enable one of ordinaryskill in the art to make and use the invention and to incorporate it inthe context of particular applications. Various modifications, as wellas a variety of uses in different applications will be readily apparentto those skilled in the art, and the general principles defined hereinmay be applied to a wide range of aspects. Thus, the present inventionis not intended to be limited to the aspects presented, but is to beaccorded the widest scope consistent with the principles and novelfeatures disclosed herein.

In order to provide a working frame of reference, first a glossary ofsome of the terms used in the description and claims is given as acentral resource for the reader. The glossary is intended to provide thereader with a general understanding of various terms as they are used inthis disclosure, but is not intended to limit the scope of these terms.Rather, the scope of the terms is intended to be construed withreference to this disclosure as a whole and with respect to the claimsbelow. In particular, because there exists a degree of vocabulary“cross-over” between the language used to describe decision flowchartsand that used to describe probabilistic graphs, this glossary serves asan aid to the reader for distinguishing between those terms used todescribe flowcharts and those used to describe probabilistic graphs.Then, a brief introduction is provided in the form of a narrativedescription of the present invention to give a conceptual understandingprior to developing the specific details.

(1) Glossary

Before describing the specific details of the present invention, it isuseful to provide a centralized location for various terms used hereinand in the claims. The terms defined are as follows:

Aggregate Outcome Node—An aggregate outcome node is a node of aprobabilistic graph that includes outcome states, and that is linkedwith test nodes. An aggregate outcome node is an aggregation of theoutcome nodes of a decision flowchart.

Conclusion Link—A conclusion link is an element of a decision flowchartrepresenting a conclusion to be drawn from a set of evidence queries ortests. In the flowchart, the conclusions are represented as linksbetween evidence nodes and other evidence nodes or between evidencenodes and outcome nodes.

Decision Flowchart—A decision flowchart is a general term used toindicate a tree-type diagram having a root evidence node and otherevidence nodes, with evidence conclusion links extending from theevidence nodes to other evidence nodes or outcome nodes (leaf nodes inthe flowchart), creating branches of the tree diagram. In an example ofa decision flowchart, the root evidence node could be a diagnostic test,which is performed to gather information useful in making a decision.Given the evidence gathered from the diagnostic test, an evidenceconclusion is made, represented by a conclusion link, and indicating anext diagnostic test to perform. After all diagnostic tests along abranch are performed a final conclusion is drawn, represented in thedecision flowchart by an outcome node.

Evidence Nodes—Evidence nodes are elements of a decision flowchart, andrepresent gathered evidence, whether collected through the performanceof a test, from existing information, or other mechanisms.

Test States—Test states are elements of a probabilistic graph,representing the conclusion links of an evidence node from a decisionflowchart. They are included as elements of corresponding test nodes inthe probabilistic graph. For example, if the evidence node represented atest, the evidence state could be either “pass” or “fail.” Depending onthe nature of the evidence nodes, there may be two or more possibleassociated test states (conclusions) for each evidence node.

Means—The term “means” as used with respect to this invention generallyindicates a set of operations to be performed on a computer.Non-limiting examples of “means” include computer program code (sourceor object code) and “hard-coded” electronics. The “means” may be storedin the memory of a computer or on a computer readable medium.

Outcome Nodes—An outcome node is an element of a decision flowchartrepresenting an outcome of, or a conclusion to be drawn from, a set ofevidence queries or tests. In the flowchart, the outcome nodes arerepresented as leaf nodes in a tree structure.

Outcome States—Outcome states are elements of an aggregate outcome nodeof a probabilistic graph, with each outcome state representing anoutcome node of a decision flowchart.

Probabilistic Graph—A probabilistic graph is a data structure comprisingan aggregate outcome node that is directly linked with test nodes. Theprobabilistic graph is an alternative representation of the decisionflowchart.

Test Node—A test node is an element of a probabilistic graph, linkedwith the aggregate outcome node. Test nodes include test states (e.g.,“pass” or “fail” in a binary state case). It is important to note thatthe term “test” used in this context may be an active test, or it may bea passive decision mechanism. Because the test node contains aspects ofthe evidence nodes and the evidence conclusions of a probabilisticflowchart, the term “test” was selected simply as a convention for moreclearly distinguishing what is intended, and not to imply any particularmeaning.

(2) Introduction

The present invention provides a mechanism for converting decisionflowcharts into probabilistic graphs. The invention can form part of anoverall decision modeling system. As shown in FIG. 1, such an overallsystem begins with a means for providing a model of a decision processin the form of a decision flowchart 100. This “means” can, for example,take the form of a software flowcharting system, a computer-aideddrawing system, or a mechanism for computer-reading manually-designedflowcharts. After a flowchart is created, it is exported to a computerfile 102. The computer file 102 may be in a format native to the programfrom which it was created, a “portable” format, or any other computerrepresentation. After the computer file 102 has been created, it isprovided to a translator 104. After translation, the file may beconverted to portable file type 106 using an approach such as anextensible markup language (XML)-based language, termed a flow-chartmarkup language (FCML). The portable file is next converted by aconverter 108 into a Bayesian network file 110. The operations of thepresent invention, in converting the decision flowchart into theprobabilistic graph, form the heart of the converter 108. Afterconversion to a Bayesian network file 110, the file can be used inconjunction with decision support software 112 and a Bayesian networkmodeling program (engine) 114. The decision support software 112 andBayesian network modeling program 114 allow the decision processembedded in the original decision flowchart to be manipulated andexecuted by user input 116.

The specific details regarding the conversion of the decision flowchartinto the probabilistic graph form the heart of the present invention,and are explained below.

(3) Physical Aspects of the Present Invention

The present invention has three principal “physical” aspects. The firstis an apparatus for converting decision flowcharts into decisionprobabilistic graphs, typically in the form of a computer systemoperating software or in the form of a “hard-coded” instruction set. Thesecond physical aspect is a method, typically in the form of software,operated using a data processing system (computer). The third principalphysical aspect is a computer program product. The computer programproduct generally represents computer readable code stored on a computerreadable medium such as an optical storage device, e.g., a compact disc(CD) or digital versatile disc (DVD), or a magnetic storage device suchas a floppy disk or magnetic tape. Other, non-limiting examples ofcomputer readable media include hard disks and flash-type memories.These aspects will be described in more detail below.

A block diagram depicting the components of a computer system used inthe present invention is provided in FIG. 2. The data processing system200 comprises an input 202 for receiving a decision flowchart. Note thatthe input 202 may include multiple “ports” for receiving data and userinput. Typically, user input is received from traditional input/outputdevices such as a mouse, trackball, keyboard, light pen, etc., but mayalso be received from other means such as voice or gesture recognitionfor example. An output 204 provides data to users or to other devices orprograms; e.g., output to a user may be provided on a video display suchas a computer screen, but may also be provided via printers or othermeans. The input 202 and the output 204 are both coupled with aprocessor 206, which may be a general-purpose computer processor or aspecialized processor designed specifically for use with the presentinvention. The processor 206 is coupled with a memory 208 to permitstorage of data and software to be manipulated by commands to theprocessor.

An illustrative diagram of a computer program product embodying thepresent invention is depicted in FIG. 3. The computer program product300 is depicted as an optical disk such as a CD or DVD. However, asmentioned previously, the computer program product generally representscomputer readable code stored on any compatible computer readablemedium.

(4) Operational Details of the Present Invention

The present invention provides a tool for the automatic generation ofgraphical probabilistic models from existing decision flowcharts. Thus,the knowledge captured in a decision flowchart can be readily convertedinto a more powerful tool, allowing alteration of the decision process,and a graphical probabilistic model generated from it, in a simple andautomatic manner, thus eliminating the need to re-start the modelingprocess from scratch. This conversion makes it possible to alleviate theshortcomings of flowcharts and to take advantage of the benefits ofprobabilistic models. The immediate benefits include flexibility of usein diagnosis, easy updating by learning, and the ability to cover costof observations and multiple faults. This tool has application in anyfield where decisions are applied, non-limiting examples of whichinclude diagnosing problems with machinery, such as cars, trucks,planes, boats, and trains, as well as with other problem types, such ascomputer network communications, satellite diagnostics, etc.

A decision flowchart is a graph comprising nodes and branches, anexample of which is shown in FIG. 4. The graph is typically in the formof a tree which, at its root 400 a, has a single node representing aninitial piece of evidence (typically an observation such as a symptom ina diagnostic problem). Branches, termed conclusion links 402, leavingthe root evidence node 400 a represent all possible outcomes of theobservation. Each of the conclusion links 402 leads to the next node onthe path. That node may be another evidence node 400 or it may be anoutcome node 404. In the example decision flowchart, as depicted in FIG.4, there are four evidence nodes 400 (including the root evidence node400 a), shown as squares and labeled T1, T2, T3, and T4. In addition,five outcome nodes 404 are shown as ovals with labels OK, F1, F3, F4,and F5. In a diagnostic application, the nodes F1, F3, F4, and F5 mayrepresent failures, where OK represents a termination of a decision paththat is not a failure. The “OK” can represent an “all is fine” condition(node) or a pointer to another flowchart, which continues diagnosis forthat branch. Each of the evidence nodes 400 in FIG. 4 has two conclusionlinks 402, representing two possible outcomes: pass or fail.

The two outcome example is provided for illustration purposes only andthe present invention is not limited to two outcomes per evidence node400. Further, there is no limitation on the number of times the sameevidence node 400 may occur in the flowchart, as long as it appears ondifferent paths. However, the same outcome node 404 typically cannotappear at the end of two or more different paths. If the repeatedoutcome node 404 represents the same component or test, then each timeit appears, it should represent a different failure mode of thecomponent. In this case, each occurrence of the outcome node 404 wouldbe labeled differently. The general steps for converting a flowchartinto a graphical probabilistic model are illustrated next, using theflowchart from FIG. 4 as a non-limiting example.

First, each of the evidence nodes 400 and the outcome nodes 404 in theflowchart is labeled, with no repeating labels for identical outcomenodes 404. In the particular case shown, T1, T2, T3, and T4 are used aslabels to represent tests to be performed to gather evidence, and OK,F1, F3, F4, and F5 are used as labels to indicate particular sources offailure (with OK, if no failure exists). Next, each conclusion link 402of each evidence node 400 in the flowchart is labeled. In the caseshown, the conclusion links 402 are labeled “PASS” or “FAIL.” After thelabeling has been completed, a graphical structure is created for theprobabilistic graph, as depicted in FIG. 5. In this case, a naive Bayesnetwork (BN) was used as the probabilistic model. An aggregate outcomenode 500 is created in the Bayesian network, labeled “Faults.” Theaggregate outcome node 500 includes states that represent all of theoutcome nodes 404 in the Bayesian network. For each of the evidencenodes 400 in the flowchart, a separate test node 502 is created in theBayesian network. For each conclusion link 402 from an evidence node 400in the flowchart, a separate test state 504 is created in the matchingtest node 502 of the Bayesian network. In the case shown, forsimplicity, the name is generated using the label of the matchingoutcome, e.g., pass/fail. Causal links 506 are then created in theBayesian network leading from the aggregate outcome node 500 to eachtest node 502.

After the Bayesian network structure has been created, priorprobabilities are derived for the states of the aggregate outcome node500 in the Bayesian network. The prior probabilities serve as theinitial conditions, and may vary depending on the requirements of aparticular implementation and the degree of pre-existing knowledgeavailable. For purposes of this discussion, an equal likelihood ofoccurrence of each outcome state of the aggregate outcome node 500 isassumed. The prior probability for each outcome state is computed inthis case by dividing one by the number of outcome states (e.g., for thesituation in FIG. 5, the prior probability for each of the outcomestates is ⅕=20%).

Next, conditional probabilities are derived for all test nodes 502 inthe Bayesian network, given any of the outcome states in the aggregateoutcome node 500. In this process, first a causal dependency table iscreated for the flowchart (a causal dependency table for the flowchartof FIG. 4 is shown in FIG. 6). The table captures the causal dependencyof each outcome state on each piece of evidence, and has a separatecolumn for each outcome node 404 in the flowchart and a separate row foreach evidence node 400. The entries in the table represent outcomes of(conclusion links 402 extending from) evidence nodes 400. The entries ineach column represent the combination that leads to the outcome stateshown in the column header. For example, to arrive at a conclusion of anF1 defect, T1 must register “pass” and T2 must register “fail”. Hyphensindicate an evidence node 400 that is not along the path from the rootevidence node 400 a to the outcome node 404. It may be observed that inthe flowchart of FIG. 4, since T1 is the root node, T1 exists in allcolumns, since the test T1 is executed for all paths in the flowchart.Thus, the entries in the table are either conclusion links 402 ofevidence nodes 400 or hyphens. As stated, each failure in the flowchartindicates the trace of a path from the conclusion node 404 to the rootevidence node 400 a. The path consists of the names of evidence nodes400 and outcome nodes 404 (e.g., for the fault F5 in the flowchart ofFIG. 4, the path is T4-Fail, T3-Pass, and T1-Fail). For each failure,the path information is entered into its column. In the rows for theevidence nodes 400 present on the path, the outcome or conclusion link402 (e.g. pass or fail) is entered. In the remaining rows, a hyphen isentered.

For each test node 502 in the Bayesian network, a conditionalprobability table is derived from the causal dependency table (shown inFIG. 6). An example of a conditional probability table is shown in FIG.7. The conditional probability table contains conditional probabilitiesof an outcome state, given a particular test state 504 of a test node502 (in this case, a failure). For example, in a diagnostic testingcase, the conditional probabilities for a particular test, T3, given theparticular defect FN are shown in the table. Since there is a hyphen inthe column F1-defect, test T3 is considered independent of (unable todetect) defect F1. Since the flowchart shown in FIG. 4 is a binary treewith two conclusion links 402 extending from each evidence node 400, andsince T3 is independent of F1, the chance of pass and the chance of failare both 50%. In essence, the outcome of test T3 is a “don't care”value, and since F1 has no impact on the outcome of T3, it does notmatter whether the outcome of T3 is pass or fail, and thus both areassigned an equal likelihood. If, for example, node T3 in FIG. 4 hadthree outcomes, and was a “don't care” value, then the conditionalprobability of each of the three outcomes (states) would be ⅓(approximately 33%). Since an F3 defect causes a failure of test T3, theconditional probability of a failure of test T3 for failure F3 in FIG. 7is shown as 1 (or 100%) for failure and 0 (0%) for pass. With regard toF4 and F5-type defects, T3 must be passed, thus, the conditionalprobability entries in these columns are 1 (or 100%) for pass and 0 (0%)for failure. Like outcome F1, outcome OK does not have an impact on T3.Thus, both pass and fail are assigned equal values of 0.5 (50%) in thiscolumn. Similar conditional probability tables are developed for eachobservation node (test TN).

Note that for a given piece of evidence and a given outcome, theconditional probabilities for all states must add up to 1 (100%). Theconditional probability values will be equal to either 0 or 1 or 1divided by the number of states (possible outcomes) of the observation.For each observation, a pertinent row in the causal dependency table isidentified and translated into conditional probabilities one column at atime (i.e., the entry from the column for a given failure will betranslated into a conditional probability of the observation given thatparticular feature). For each column with a hyphen in the causaldependency table, a value equal to one divided by the number of statesfor that observation is entered. Thus, the possible outcomes of a testunrelated to the failure in question are considered as “don't care”values with an equal likelihood of occurrence. For all other columns,the state in the conditional probability table that matches the outcomelisted in the causal dependency table is identified, and the conditionalprobability for that state is set to 1 (100%), and the probability forremaining states are set to 0 (0%).

This procedure can be embedded into a software tool. The tool receivesthe flowchart and produces, as output, a model file for the Bayesiannetwork. The Bayesian model used with an algorithmic engine producesdecision sequences. The format of the Bayesian network file is dependenton the algorithmic engine. Examples of widely used Bayesian networkengines include GeNIe (produced by Decision Systems Laboratory,University of Pittsburgh), Hugin (produced by Hugin, Inc., Niels JemesVej 10, 9220 Aalborg, Denmark), and Netica (produced by Norsys SoftwareCorp., 2315 Dunbar Street, Vancouver, BC, Canada V6R 3N1). Somealgorithmic engines are capable of translating model file formats ofother engines. The procedure has been tested on an engine capable ofproducing ranked failures and ranked observations at each decisioniteration. It ranks the failures using probability and observationsusing entropy calculations. The probabilistic model produced by theprocedure described herein results in decision sequences of optimalconvergence (i.e., a minimal number of tests being required to arrive ata root-cause).

The model produced by this procedure may also be enhanced usingadditional information. For example, costs of observations, ifavailable, can be added directly to the model. If the actual likelihoodof failures of components is known, the equal distribution of failuresused in the procedure described above can be replaced by actualstatistics. Such a model would produce decision sequences optimized notonly for convergence, but also for cost and failure likelihood. Themodel produced by the procedure shown above is a single-fault model, andcan be easily modified into a multiple-fault model if additionalprobability information is available.

1. A method for converting decision flowcharts into decisionprobabilistic graphs on a data processing system comprising steps of:receiving a representation of a decision flowchart having evidencenodes, a root evidence node, and outcome nodes, where the outcome nodesare related to the evidence nodes by conclusion links; generating aprobabilistic graph based on the decision flowchart, including: anaggregate outcome node having a plurality of outcome states, with eachoutcome state representing an outcome node of the decision flowchart; aplurality of test nodes with each of the test nodes matching an evidencenode in the decision flowchart, and each test state matching aconclusion link from the evidence node in the flowchart; causal linksbetween the aggregate outcome node and the evidence nodes; calculating aset of prior probabilities for the outcome states; and determiningconditional probabilities for all test states by examining dependenciesof conclusion links on the outcome nodes in the decision flowchart.
 2. Amethod for converting decision flowcharts into decision probabilisticgraphs on a data processing system as set forth in claim 1, wherein therepresentation of the decision flowchart is obtained as a FlowchartMarkup Language (FCML) document containing the essence of the decisionflowchart.
 3. A method for converting decision flowcharts into decisionprobabilistic graphs on a data processing system as set forth in claim2, wherein the graphical representation of the decision flowchart is aBayesian Network (BN).
 4. A method for converting decision flowchartsinto decision probabilistic graphs on a data processing system as setforth in claim 3, wherein in the step of calculating a set ofconditional probabilities, a sub-step of generating a causal dependencytable comprising a causal dependency of each test node on each outcomestate is performed.
 5. A method for converting decision flowcharts intodecision probabilistic graphs on a data processing system as set forthin claim 4, wherein the causal dependency table includes a separatecolumn for each outcome node of the decision flowchart and a separaterow for each evidence node of the decision flowchart; whereby aggregateentries of each column of the table trace a path from the root evidencenode to a particular outcome node.
 6. A method for converting decisionflowcharts into decision probabilistic graphs on a data processingsystem as set forth in claim 5, wherein the step of determiningconditional probabilities further comprises a sub-step of: generating,for each evidence node, a conditional probability table comprising theconditional probability of each test state given each outcome state. 7.A method for converting decision flowcharts into decision probabilisticgraphs on a data processing system as set forth in claim 6, furthercomprising a step of: generating a model file for the probabilisticgraph to an algorithmic engine for further processing.
 8. A method forconverting decision flowcharts into decision probabilistic graphs on adata processing system as set forth in claim 7, wherein thepredetermined likelihoods are inputted based on observed statistics. 9.A method for converting decision flowcharts into decision probabilisticgraphs on a data processing system as set forth in claim 8, comprising afurther step of determining a next piece of evidence to gather based oncost-of-evidence data.
 10. A method for converting decision flowchartsinto decision probabilistic graphs on a data processing system as setforth in claim 9, comprising a further step of generating arepresentation of the decision flowchart via graphing software forreceipt in the receiving step.
 11. A method for converting decisionflowcharts into decision probabilistic graphs on a data processingsystem as set forth in claim 10, comprising a further step of generatinga user interface to accept user input to the algorithmic engine wherebythe user can control the order in which evidence is collected.
 12. Amethod for converting decision flowcharts into decision probabilisticgraphs on a data processing system as set forth in claim 11, furthercomprising a step of converting the graphical representation of thedecision flowchart into a Bayesian network program-specific file.
 13. Amethod for converting decision flowcharts into decision probabilisticgraphs on a data processing system as set forth in claim 1, wherein thegraphical representation of the decision flowchart is a Bayesian Network(BN).
 14. A method for converting decision flowcharts into decisionprobabilistic graphs on a data processing system as set forth in claim1, wherein in the step of calculating a set of conditionalprobabilities, a sub-step of generating a causal dependency tablecomprising a causal dependency of each test node on each outcome stateis performed.
 15. A method for converting decision flowcharts intodecision probabilistic graphs on a data processing system as set forthin claim 14, wherein the causal dependency table includes a separatecolumn for each outcome node of the decision flowchart and a separaterow for each evidence node of the decision flowchart; whereby aggregateentries of each column of the table trace a path from the root evidencenode to a particular outcome node.
 16. A method for converting decisionflowcharts into decision probabilistic graphs on a data processingsystem as set forth in claim 1, wherein the step of determiningconditional probabilities further comprises a sub-step of: generating,for each evidence node, a conditional probability table comprising theconditional probability of each test state given each outcome state. 17.A method for converting decision flowcharts into decision probabilisticgraphs on a data processing system as set forth in claim 1, furthercomprising a step of: generating a model file for the probabilisticgraph to an algorithmic engine for further processing.
 18. A method forconverting decision flowcharts into decision probabilistic graphs on adata processing system as set forth in claim 1, wherein thepredetermined likelihoods are inputted based on observed statistics. 19.A method for converting decision flowcharts into decision probabilisticgraphs on a data processing system as set forth in claim 1, comprising afurther step of determining a next piece of evidence to gather based oncost-of-evidence data.
 20. A method for converting decision flowchartsinto decision probabilistic graphs on a data processing system as setforth in claim 1, comprising a further step of generating arepresentation of the decision flowchart via graphing software forreceipt in the receiving step.
 21. A method for converting decisionflowcharts into decision probabilistic graphs on a data processingsystem as set forth in claim 1, comprising a further step of generatinga user interface to accept user input to the algorithmic engine wherebythe user can control the order in which evidence is collected.
 22. Amethod for converting decision flowcharts into decision probabilisticgraphs on a data processing system as set forth in claim 1, furthercomprising a step of converting the graphical representation of thedecision flowchart into a Bayesian network program-specific file.
 23. Acomputer program product for converting decision flowcharts intodecision probabilistic graphs, the computer program product comprisingmeans, encoded in a computer-readable medium for: receiving arepresentation of a decision flowchart having evidence nodes, a rootevidence node, and outcome nodes, where the outcome nodes are related tothe evidence nodes by conclusion links; generating a probabilistic graphbased on the decision flowchart, including: an aggregate outcome nodehaving a plurality of outcome states, with each outcome staterepresenting an outcome node of the decision flowchart; a plurality oftest nodes with each of the test nodes matching an evidence node in thedecision flowchart, and each test state matching a conclusion link fromthe evidence node in the flowchart; causal links between the aggregateoutcome node and the evidence nodes; calculating a set of priorprobabilities for the outcome states; and determining conditionalprobabilities for all test states by examining dependencies ofconclusion links on the outcome nodes in the decision flowchart.
 24. Acomputer program product for converting decision flowcharts intodecision probabilistic graphs on a data processing system as set forthin claim 23, wherein the representation of the decision flowchart isobtained as a Flowchart Markup Language (FCML) document containing theessence of the decision flowchart.
 25. A computer program product forconverting decision flowcharts into decision probabilistic graphs on adata processing system as set forth in claim 24, wherein the graphicalrepresentation of the decision flowchart is a Bayesian Network (BN). 26.A computer program product for converting decision flowcharts intodecision probabilistic graphs on a data processing system as set forthin claim 25, wherein the means for calculating a set of conditionalprobabilities includes means for generating a causal dependency tablecomprising a causal dependency of each test node on each outcome stateis performed.
 27. A computer program product for converting decisionflowcharts into decision probabilistic graphs on a data processingsystem as set forth in claim 26, wherein the causal dependency tableincludes a separate column for each outcome node of the decisionflowchart and a separate row for each evidence node of the decisionflowchart; whereby aggregate entries of each column of the table trace apath from the root evidence node to a particular outcome node.
 28. Acomputer program product for converting decision flowcharts intodecision probabilistic graphs on a data processing system as set forthin claim 27, wherein the means for determining conditional probabilitiesfurther includes means for: generating, for each evidence node, aconditional probability table comprising the conditional probability ofeach test state given each outcome state.
 29. A computer program productfor converting decision flowcharts into decision probabilistic graphs ona data processing system as set forth in claim 28, further comprisingmeans for: generating a model file for the probabilistic graph to analgorithmic engine for further processing.
 30. A computer programproduct for converting decision flowcharts into decision probabilisticgraphs on a data processing system as set forth in claim 29, furthercomprising means for accepting predetermined likelihoods based onobserved statistics.
 31. A computer program product for convertingdecision flowcharts into decision probabilistic graphs on a dataprocessing system as set forth in claim 30, further comprising means fordetermining a next piece of evidence to gather based on cost-of-evidencedata.
 32. A computer program product for converting decision flowchartsinto decision probabilistic graphs on a data processing system as setforth in claim 31, further comprising means for generating arepresentation of the decision flowchart via graphing software forreceipt by the means for receiving.
 33. A computer program product forconverting decision flowcharts into decision probabilistic graphs on adata processing system as set forth in claim 32, further comprisingmeans for generating a user interface to accept user input to thealgorithmic engine whereby the user can control the order in whichevidence is collected.
 34. A computer program product for convertingdecision flowcharts into decision probabilistic graphs on a dataprocessing system as set forth in claim 33, further comprising means forconverting the graphical representation of the decision flowchart into aBayesian network program-specific file.
 35. A computer program productfor converting decision flowcharts into decision probabilistic graphs ona data processing system as set forth in claim 23, wherein the graphicalrepresentation of the decision flowchart is a Bayesian Network (BN). 36.A computer program product for converting decision flowcharts intodecision probabilistic graphs on a data processing system as set forthin claim 23, wherein the means for calculating a set of conditionalprobabilities further includes means for generating a causal dependencytable comprising a causal dependency of each test node on each outcomestate is performed.
 37. A computer program product for convertingdecision flowcharts into decision probabilistic graphs on a dataprocessing system as set forth in claim 36, wherein the causaldependency table includes a separate column for each outcome node of thedecision flowchart and a separate row for each evidence node of thedecision flowchart; whereby aggregate entries of each column of thetable trace a path from the root evidence node to a particular outcomenode.
 38. A computer program product for converting decision flowchartsinto decision probabilistic graphs on a data processing system as setforth in claim 23, wherein the means for determining conditionalprobabilities further comprises means for: generating, for each evidencenode, a conditional probability table comprising the conditionalprobability of each test state given each outcome state.
 39. A computerprogram product for converting decision flowcharts into decisionprobabilistic graphs on a data processing system as set forth in claim23, further comprising means for: generating a model file for theprobabilistic graph to an algorithmic engine for further processing. 40.A computer program product for converting decision flowcharts intodecision probabilistic graphs on a data processing system as set forthin claim 23, further comprising means for accepting predeterminedlikelihoods based on observed statistics.
 41. A computer program productfor converting decision flowcharts into decision probabilistic graphs ona data processing system as set forth in claim 23, further comprisingmeans for determining a next piece of evidence to gather based oncost-of-evidence data.
 42. A computer program product for convertingdecision flowcharts into decision probabilistic graphs on a dataprocessing system as set forth in claim 23, further comprising means forgenerating a representation of the decision flowchart via graphingsoftware for receipt by the means for receiving.
 43. A computer programproduct for converting decision flowcharts into decision probabilisticgraphs on a data processing system as set forth in claim 23, furthercomprising means for generating a user interface to accept user input tothe algorithmic engine whereby the user can control the order in whichevidence is collected.
 44. A computer program product for convertingdecision flowcharts into decision probabilistic graphs on a dataprocessing system as set forth in claim 23, further comprising means forconverting the graphical representation of the decision flowchart into aBayesian network program-specific file.
 45. An apparatus for convertingdecision flowcharts into decision probabilistic graphs, the apparatuscomprising a computer system including a processor, a memory coupledwith the processor, an input coupled with the processor for receivinguser input and data input, and an output coupled with the processor foroutputting display data, wherein the computer system further comprisesmeans, residing in its processor and memory, for: receiving arepresentation of a decision flowchart having evidence nodes, a rootevidence node, and outcome nodes, where the outcome nodes are related tothe evidence nodes by conclusion links; generating a probabilistic graphbased on the decision flowchart, including: an aggregate outcome nodehaving a plurality of outcome states, with each outcome staterepresenting an outcome node of the decision flowchart; a plurality oftest nodes with each of the test nodes matching an evidence node in thedecision flowchart, and each test state matching a conclusion link fromthe evidence node in the flowchart; causal links between the aggregateoutcome node and the evidence nodes; calculating a set of priorprobabilities for the outcome states; and determining conditionalprobabilities for all test states by examining dependencies ofconclusion links on the outcome nodes in the decision flowchart.
 46. Anapparatus for converting decision flowcharts into decision probabilisticgraphs on a data processing system as set forth in claim 45, wherein therepresentation of the decision flowchart is obtained as a FlowchartMarkup Language (FCML) document containing the essence of the decisionflowchart.
 47. An apparatus for converting decision flowcharts intodecision probabilistic graphs on a data processing system as set forthin claim 46, wherein the graphical representation of the decisionflowchart is a Bayesian Network (BN).
 48. An apparatus for convertingdecision flowcharts into decision probabilistic graphs on a dataprocessing system as set forth in claim 47, wherein the means forcalculating a set of conditional probabilities includes means forgenerating a causal dependency table comprising a causal dependency ofeach test node on each outcome state is performed.
 49. An apparatus forconverting decision flowcharts into decision probabilistic graphs on adata processing system as set forth in claim 48, wherein the causaldependency table includes a separate column for each outcome node of thedecision flowchart and a separate row for each evidence node of thedecision flowchart; whereby aggregate entries of each column of thetable trace a path from the root evidence node to a particular outcomenode.
 50. An apparatus for converting decision flowcharts into decisionprobabilistic graphs on a data processing system as set forth in claim49, wherein the means for determining conditional probabilities furtherincludes means for: generating, for each evidence node, a conditionalprobability table comprising the conditional probability of each teststate given each outcome state.
 51. An apparatus for converting decisionflowcharts into decision probabilistic graphs on a data processingsystem as set forth in claim 50, further comprising means for:generating a model file for the probabilistic graph to an algorithmicengine for further processing.
 52. An apparatus for converting decisionflowcharts into decision probabilistic graphs on a data processingsystem as set forth in claim 51, further comprising means for acceptingpredetermined likelihoods based on observed statistics.
 53. An apparatusfor converting decision flowcharts into decision probabilistic graphs ona data processing system as set forth in claim 52, further comprisingmeans for determining a next piece of evidence to gather based oncost-of-evidence data.
 54. An apparatus for converting decisionflowcharts into decision probabilistic graphs on a data processingsystem as set forth in claim 53, further comprising means for generatinga representation of the decision flowchart via graphing software forreceipt by the means for receiving.
 55. An apparatus for convertingdecision flowcharts into decision probabilistic graphs on a dataprocessing system as set forth in claim 54, further comprising means forgenerating a user interface to accept user input to the algorithmicengine whereby the user can control the order in which evidence iscollected.
 56. An apparatus for converting decision flowcharts intodecision probabilistic graphs on a data processing system as set forthin claim 55, further comprising means for converting the graphicalrepresentation of the decision flowchart into a Bayesian networkprogram-specific file.
 57. An apparatus for converting decisionflowcharts into decision probabilistic graphs on a data processingsystem as set forth in claim 45, wherein the graphical representation ofthe decision flowchart is a Bayesian Network (BN).
 58. An apparatus forconverting decision flowcharts into decision probabilistic graphs on adata processing system as set forth in claim 45, wherein the means forcalculating a set of conditional probabilities further includes meansfor generating a causal dependency table comprising a causal dependencyof each test node on each outcome state is performed.
 59. An apparatusfor converting decision flowcharts into decision probabilistic graphs ona data processing system as set forth in claim 58, wherein the causaldependency table includes a separate column for each outcome node of thedecision flowchart and a separate row for each evidence node of thedecision flowchart; whereby aggregate entries of each column of thetable trace a path from the root evidence node to a particular outcomenode.
 60. An apparatus for converting decision flowcharts into decisionprobabilistic graphs on a data processing system as set forth in claim45, wherein the means for determining conditional probabilities furthercomprises means for: generating, for each evidence node, a conditionalprobability table comprising the conditional probability of each teststate given each outcome state.
 61. An apparatus for converting decisionflowcharts into decision probabilistic graphs on a data processingsystem as set forth in claim 45, further comprising means for:generating a model file for the probabilistic graph to an algorithmicengine for further processing.
 62. An apparatus for converting decisionflowcharts into decision probabilistic graphs on a data processingsystem as set forth in claim 45, further comprising means for acceptingpredetermined likelihoods based on observed statistics.
 63. An apparatusfor converting decision flowcharts into decision probabilistic graphs ona data processing system as set forth in claim 45, further comprisingmeans for determining a next piece of evidence to gather based oncost-of-evidence data.
 64. An apparatus for converting decisionflowcharts into decision probabilistic graphs on a data processingsystem as set forth in claim 45, further comprising means for generatinga representation of the decision flowchart via graphing software forreceipt by the means for receiving.
 65. An apparatus for convertingdecision flowcharts into decision probabilistic graphs on a dataprocessing system as set forth in claim 45, further comprising means forgenerating a user interface to accept user input to the algorithmicengine whereby the user can control the order in which evidence iscollected.
 66. An apparatus for converting decision flowcharts intodecision probabilistic graphs on a data processing system as set forthin claim 45, further comprising means for converting the graphicalrepresentation of the decision flowchart into a Bayesian networkprogram-specific file.